Prediction and control of COVID-19 infection based on a hybrid intelligent model

Zhang, G.; Liu, X.

The coronavirus (COVID-19) is a highly infectious disease that emerged in the late December 2019 in Wuhan, China, and it has caused a worldwide outbreak, which represents a major threat to global health. It is important to design prediction research and control strategies to crush its exploding. In this study, a hybrid intelligent model is proposed to simulate the spreading dynamics of COVID-19. First, considering the control measures, such as government investment, media publicity, medical treatment and law enforcement. The infection rates are optimized by genetic algorithm (GA), then a modified susceptible-infected-quarantined-recovered (SIQR) model is proposed, then the long short-term memory (LSTM) is imbedded into the SIQR model to design the hybrid intelligent model to further optimize other parameters of the system model to obtain the optimal predictive model and control measures. This study provide a reliable model to predict cases of infection and death, and reasonable suggestion to control COVID-19.